Sequencing Mixed-Model Assembly Lines to Minimize the Variation of Parts Consumption by Hybrid Genetic Algorithms

2012 ◽  
Vol 566 ◽  
pp. 253-256
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Initial Population ◽  
Assembly Lines ◽  
Mixed Model Assembly Lines ◽  
Mutation Operators ◽  
Sequencing Problems ◽  
Crossover And Mutation ◽  
Mixed Model Assembly

This paper is concerned about the sequencing problems in mixed-model assembly lines. The optimization objective is to minimizing the variation of parts consumption. The mathematical models are put forward. Since the problem is NP-hard, a hybrid genetic algorithm is newly-designed for solving the models. In this algorithm, the new method of forming the initial population is presented, the hybrid crossover and mutation operators are adopted, and moreover, the adaptive probability values for performing the crossover and mutation operations are used. The optimization performance is compared between the hybrid genetic algorithm and a genetic algorithm proposed in early published literature. The computational results show that satisfactory solutions can be obtained by the hybrid genetic algorithm and it performs better in terms of solution’s quality.

A Modified Genetic Algorithm for Sequencing Problem in Mixed Model Assembly Lines

2013 ◽  
Vol 655-657 ◽  
pp. 1675-1681
Author(s):  
Shu Xu ◽  
Fu Ming Li
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Assembly Lines ◽  
Sequencing Problem ◽  
Mixed Model Assembly Lines ◽  
Modified Genetic Algorithm ◽  
Good Ability ◽  
Sequencing Problems ◽  
Dependent Setup Times ◽  
Mixed Model Assembly

On the base of summarizing and contrasting the objectives of sequencing problem in mixed model assembly lines (MMAL) , and in consideration of the influence sequence-dependent setup times , a objective is proposed to minimize the total unfinished works and idle times over all jobs and stations . And the corresponding model is presented. To solve this model, a modified genetic algorithm is proposed to determine suitable sequences. Comparing with the Lingo 9 software, the proposed GA turns out to have a good ability to solve the sequencing problems.

Balancing mixed-model assembly lines with sequence-dependent tasks via hybrid genetic algorithm

2015 ◽  
Vol 65 (1) ◽  
pp. 83-107 ◽  
Author(s):  
Qiuhua Tang ◽  
Yanli Liang ◽  
Liping Zhang ◽  
Christodoulos A. Floudas ◽  
Xiaojun Cao
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Hybrid Genetic Algorithm ◽  
Assembly Lines ◽  
Mixed Model Assembly Lines ◽  
Sequence Dependent ◽  
Dependent Tasks ◽  
Mixed Model Assembly

A genetic algorithm for supermarket location problem

2015 ◽  
Vol 35 (1) ◽  
pp. 122-127 ◽  
Author(s):  
Mohammed Alnahhal ◽  
Bernd Noche
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Location Problem ◽  
Programming Model ◽  
Fixed Costs ◽  
Assembly Lines ◽  
Content Type ◽  
Mixed Model Assembly Lines ◽  
Mutation Operators ◽  
First Time

Purpose – This purpose of this paper is to investigate the location problem of supermarkets, feeding by material the mixed model assembly lines using tow trains. It determines the number and the locations of these supermarkets to minimize transportation and inventory fixed costs of the system. Design/methodology/approach – This is done using integer programming model and real genetic algorithm (RGA) in which custom chromosomes representation, two custom mating and two custom mutation operators were proposed. Findings – The performance of RGA is very good since it gives results that are very close or identical to the optimal ones in reasonable CPU time. Research limitations/implications – The study is applicable only if a group of supermarkets feed the same assembly line. Originality/value – For the first time in supermarket location problem, limitation on availability of some areas for possible supermarkets ' locations and capacity of the supermarkets were taken into consideration.

Mixed Model Assembly Line Sequencing by Minimizing Utility Work and Using Genetic Algorithm

2014 ◽  
Author(s):  
Uzair Khaleeq uz Zaman ◽  
Aamer Ahmed Baqai
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Assembly Lines ◽  
Setup Cost ◽  
Total Utility ◽  
Sequencing Problems ◽  
Integral Role ◽  
The Impact ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

Owing to the recent developments in the field of industrial automation, assembly lines have played an integral role in the economic uplift of the industrial units. Mixed Model Assembly Lines are the answer to a variety of scenarios which involve customized production following a particular ‘product mix’, i.e., several models of a product are jointly processed on a line, in an increased quantity, quality and productive environment. Hence, to determine the optimal operating schedule/sequence of the operations along with other impacting factors such as total utility work, setup cost, part consumption rates, etc., still remains a widely researched topic today. Moreover, sequencing problems are termed as NP-hard and a variety of sequencing heuristics have been applied in literature to solve them. The heuristic, Genetic Algorithm, was formulated based on binary encoding/decoding, two point cross over and uniform mutation, and applied in this paper to optimize two objectives; one, to minimize total utility work and two, to generate sequence of the models as per the first goal. A methodology was hence, developed to test and analyze the impact of factors such as number of stations, length of stations, conveyor speed, time of operations, number of primary models, and Minimum Part Set on the concerned objectives. An attempt was also made to model the entire process with IDEF0 modeling technique. Industry-oriented problems were then presented to test the algorithm in real world conditions. Finally, the results were critically examined and respective improvement measures were stated.

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